Currently, most display devices have a limited dynamic range which is lower than that in real world scenes. HDR scenes shown on low dynamic range (LDR) display devices usually turn out to be either saturated which corresponds to overexposure in photography or extremely dark which corresponds to underexposure. Either case is undesired as numerous details can be lost.
The amount of visual content in HDR video format has been greatly increasing. Hence, tone mapping for HDR video has drawn much attention in academia as well as in industry. However, compared with the tone mapping of still images, relatively very little effort has been put on HDR video tone mapping.
For video scenes with relatively static lighting conditions, known parameter estimation methods have performed well. In such cases, generally a set of fixed parameters are applied to all frames in the same video segment with static lighting condition.
However, for varying lighting conditions within a scene of video, the application of a fixed set of parameters to frames has produced poor tone mapping results, because the tone mapping parameters can change from one frame to another. As such, a need exists for a tone mapping process that can properly and effectively perform tone mapping in scenes with varying lighting.